Data-driven e-business environments have to deal with overwhelmingly large amounts of data. These large amounts of data can make it difficult to monitor the general health of an e-business system and answer everyday business questions such as whether a portal is functioning normally or why business today is different from business yesterday or why it is different from business the same day last week.
Data sources may be spread across a company. For example, computer usage information may be used by an Information Technology (IT) division of the company and sales information may be used by sales and marketing divisions of the company. Some data sources may give identical results, whereas other data sources may become outdated and have no impact on the business. All of these diverse types of information may create a garbage effect that obscures important business events in the enterprise system.
A selective individual (univariate) statistical approach may heuristically pick important sources of information for monitoring in the enterprise system but may not identify other important events that may be the cause of a system abnormality. Monitoring individual data items also may produce false alarms that can diminish the credibility of the reporting system.